FAIR Computational Workflows

Abstract:

Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.

SEEK ID: https://workflowhub.eu/publications/7

DOI: 10.1162/dint_a_00033

Teams: FAIR Computational Workflows

Publication type: Journal

Journal: Data Intelligence

Citation: Data Intellegence 2(1-2):108-121

Date Published: 2020

Registered Mode: by DOI

Authors: Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober

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Citation
Goble, C., Cohen-Boulakia, S., Soiland-Reyes, S., Garijo, D., Gil, Y., Crusoe, M. R., Peters, K., & Schober, D. (2020). FAIR Computational Workflows. In Data Intelligence (Vol. 2, Issues 1-2, pp. 108–121). MIT Press - Journals. https://doi.org/10.1162/dint_a_00033
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Created: 1st Dec 2021 at 21:43

Last updated: 1st Dec 2021 at 21:44

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